Industrial process facilities, such as chemical, petroleum, and petrochemical processing facilities, typically include a plurality of separate process units or sections thereof that function together to achieve the overall objective of the facility. This separation of function among the process units can lead to difficulties in effectively controlling and optimizing facility operation. These difficulties result from the wide variety of separate process units and equipment that may be contained in each facility, as well as the large number of process potential feedstocks and feedstock compositions, operating variables (e.g., flow rates, temperatures, pressures, etc.), product specifications, market constraints and prices (e.g., for feeds, products, and utilities), mechanical constraints, transportation and storage constraints, and weather conditions.
In an attempt to overcome these difficulties, computer models have been developed which can be used to accurately simulate and/or optimize the facility's operation. Two classes of models have been developed: first principles reference tools and derived tools. First principles reference tools are models that are based on first principles i.e., mathematical relationships or logic that utilize accepted scientific theories or laws, such as those regarding chemical thermodynamics and/or kinetics. Such tools typically possess the capability to separately model many or all of the individual process units in a process facility. First principles reference tools typically contain a library of thermodynamic information relating to the behavior of different molecules, components, or pseudo-components in these process units. These tools can be used to create a model of a process facility, or section thereof, by using the thermodynamic library to individually model the various process units in the facility. The model can then be used to simulate connections between process units to model the overall facility. For example, such a model can then directly provide heat and material balance information, which can be used for design, equipment rating, equipment performance, simulation, and optimization of the facility. Examples of commercially-available first principles reference tools include HYSIS® and Aspen Plus®, which are products of Aspen Technologies Incorporated of Cambridge, Mass.; PRO/II®, which is a product of SimSci-Esscor, an operating unit of Invensys plc of Cheshire, United Kingdom; and SPYRO®, which is a product of Technip-Coflexip SA of Paris, France.
Recently, a new generation of first principles reference tools has been developed for modeling, solving, and optimizing an entire process facility. Examples of these new reference tools are AspenTech RT-OPT®, which is a product of Aspen Technologies Incorporated of Cambridge, Mass., and SimSci ROMeo®, which is a product of SimSci-Esscor, an operating unit of Invensys plc, of Cheshire, United Kingdom. These tools are capable of solving very large simulation or optimization problems, usually via a non-linear simultaneous equation solver and/or optimizer.
Derived tools require less computing power and time than do first-principles tools to solve a problem of similar size and complexity. Derived tools possess very convenient structures, albeit simplified, to depict many or all of the process unit operations needed to model a process facility. These derived tools also have convenient report writing capabilities, and may possess various analysis tools for placing the modeling results in a form that can be more readily implemented. In general, derived tools use either linear programming (LP) or sequential linear programming (SLP) type mathematics to solve optimization problems.
Derived tools do not have the capability to model process unit operations based on first principles, nor do they contain a thermodynamic library. Consequently, these derived tools cannot directly provide heat and material balance information for use in design, equipment rating, equipment performance, simulation, and optimization of the facility. Instead, a derived tool model typically utilizes information about the facility that has been obtained from (i) one or more of the first principles tools (e.g, HYSIS®, Aspen Plus®, PRO/II®, and SPYRO®, referred to above), and/or (ii) other commercially available engineering tools that would be well known to persons skilled in the art of modeling industrial process facilities. This information is then imported into the derived tool.
Nevertheless, given convenient form and analysis capabilities, as well as the computing advantages of LP or SLP programming, derived tools found use in operational planning, feedstock selection, and optimization of manufacturing facilities. Examples of commercially available derived tools are AspenTech PIMS®, which is a product of Aspen Technology Incorporated of Cambridge, Mass., and SimSci Petro®, which is a product of SimSci-Esscor, an operating unit of Invensys plc., of Cheshire, United Kingdom.
More recently, models based on a combination of first principles reference tools and derived tools have been developed for large process facilities. Such models typically treat a large processing facility as two or more facilities, where each facility is broken into two or more separate models of individual process units and interconnected to represent the overall facility. Methods that have been developed utilizing first principles reference tools, derived tools, or a combination thereof for simulating a process facility will now be described.
U.S. Patent Application Publication No. 2003/0097243 A1 discloses a computerized system and method for operating a hydrocarbon or chemical production facility, comprising mathematically modeling the facility; optimizing the mathematical model with a combination of linear and non-linear solvers; and generating one or more product recipes based upon the optimized solution. In one aspect, the mathematical model further comprises a plurality of process equations having process variables and corresponding coefficients. Typically, these process variables and corresponding coefficients are used to create a matrix in a linear program. The linear program may be executed by recursion or distributed recursion. Upon successive recursive passes, updated values for a portion of the process variables and corresponding coefficients are calculated by the linear solver and by a non-linear solver, and the updated values for the process variables and corresponding coefficients are substituted into the matrix. Unfortunately, the simultaneous use of multiple solvers, some of which are non-linear, can result in significant computing time and resource disadvantages.
U.S. Pat. No. 5,666,297 discloses a software system for simulating and optimizing a processing plant design. The software system includes a plurality of dual mode equipment models for simulating each piece of equipment in the processing plant design. A sequential modular simulation routine uses the equipment models in a first mode to define a first set of values of the operating parameters of the processing plant design. Then, a simultaneous simulation/optimization routine utilizes the first set of values for the plant's operating parameters from the sequential simulation routine and subsequently determines, using the equipment models in a second mode, a second set of values of the operating parameters at which the processing plant design is optimized. The first and second sets of values for the operating parameters are stored in a common plant model file.
U.S. Pat. No. 6,442,513 discloses a method for real-time optimization of an oil refinery, or a portion thereof, where a fluid stream having multiple compositional components is modeled as a plurality of pseudo-components. Each compositional component has a boiling point, and each pseudo-component has a pre-defined boiling point and includes all compositional components from the fluid stream having approximately the pre-defined boiling point. According to this patent, good modeling results may be obtained by grouping compounds and molecules into pseudo-components or lumps based on boiling points, and by defining each input and output to a particular model according to the lumps. This is especially true in view of the fact that much of the operation of a refinery depends on boiling points of compositional components of crude oil.
U.S. Pat. No. 6,721,610 discloses a method for pre-calculating the parameters of industrial processes and/or products. According to this method, a vector of admissible input variables of the industrial process and/or product is defined. Definition ranges are assigned to each variable in the input vector. A process output vector is determined containing the process parameters to be pre-calculated. Known information on the process is stored in a data bank and ranges of validity for the process input variables are allocated to this information. For each process input vector inputted from an admissible definition range provided with valid information, exactly one process output vector is determined according to the information.
U.S. Pat. No. 7,257,451 discloses a method for creating an LP model of an industrial process facility from a first principles reference tool to interactively simulate and/or optimize the operation of the facility to facilitate or optimize feedstock selection and/or economic analyses based on varying prices, availabilities, and other external constraints.
U.S. Pat. No. 8,775,138 discloses methods and systems for withdrawing a stream from an LP model of a manufacturing facility. These methods and systems are useful for simulating the impact of partially withdrawing intermediate streams from within a process facility to simulate the impact on products and facilities in order to interactively simulate and/or optimize the operation of the facility to facilitate or optimize feedstock selection and/or economic analyses based on varying prices, availabilities, and other external constraints.
However, these previously developed methods are limited to using models to simulate and optimize a process facility as a standalone entity. As such, they fail to identify optimization possibilities that might be realized by interconnecting two or more process facilities. There is a need, therefore, for methods which can use models to simultaneously optimize a plurality of interconnected process facilities.